Comparison with baselines.
As shown in Table REF , our method outperforms baselines in all metrics with a large margin. It can generate precise boxes as well as detect many novel objects.
OLN-Faster [1]} demonstrates strong performance in AR, but performs poorly in AP because it generates many false positives. Similarly, \(\text{Mask RCNN}^{\text{S}}\)  improves performance on AR while degrading AP. Both of these baselines are thresholding background regions based on IoU with ground truth boxes, which may result in unbalanced sampling with respect to the box size. For example, if we ignore background boxes with small IoU, small regions are unlikely to become negatives and the proposal network mistakenly detects many small background regions as foreground. \(\text{Mask RCNN}^{\text{P}}\)  does not improve on Mask RCNN.
Some visualiations are illustrated in
Fig. REF .
Mask RCNN tends to overlook non-VOC class objects even when they are in the dominant and salient region, , trash can (leftmost column) and giraffes (second leftmost column). On the other hand, LDET generalizes well to novel objects such as comb, towel, giraffes, pen, phone, kites, and floats.
